# Where Responsible AI meets Reality: Practitioner Perspectives on Enablers for shifting Organizational Practices

arXiv: https://arxiv.org/abs/2006.12358 · HTML: https://mlsanity.com/paper/2006.12358/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2020.

## Built on by (most cited first, 30 of 39)

- [Interactive Model Cards: A Human-Centered Approach to Model Documentation](https://mlsanity.com/paper/2205.02894/) (2022)
- [Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits](https://mlsanity.com/paper/2205.06922/) (2022)
- [Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practice](https://mlsanity.com/paper/2210.03709/) (2022)
- [Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata](https://mlsanity.com/paper/2206.02923/) (2022)
- [Farsight: Fostering Responsible AI Awareness During AI Application Prototyping](https://mlsanity.com/paper/2402.15350/) (2024)
- [From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible ML](https://mlsanity.com/paper/2210.03535/) (2022)
- [Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences](https://mlsanity.com/paper/2310.04621/) (2023)
- [Seamful XAI: Operationalizing Seamful Design in Explainable AI](https://mlsanity.com/paper/2211.06753/) (2022)
- [Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience](https://mlsanity.com/paper/2302.10395/) (2023)
- [`It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Values](https://mlsanity.com/paper/2307.10221/) (2023)
- [User Experience Design Professionals' Perceptions of Generative Artificial Intelligence](https://mlsanity.com/paper/2309.15237/) (2023)
- [Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"](https://mlsanity.com/paper/2302.01448/) (2023)
- [Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practice](https://mlsanity.com/paper/2306.06542/) (2023)
- [Beyond the Comfort Zone: Emerging Solutions to Overcome Challenges in Integrating LLMs into Software Products](https://mlsanity.com/paper/2410.12071/) (2024)
- [A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities](https://mlsanity.com/paper/2210.08906/) (2022)
- [Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions](https://mlsanity.com/paper/2107.09546/) (2021)
- [Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliance](https://mlsanity.com/paper/2207.01482/) (2022)
- [From Hazard Identification to Controller Design: Proactive and LLM-Supported Safety Engineering for ML-Powered Systems](https://mlsanity.com/paper/2502.07974/) (2025)
- [Is a Seat at the Table Enough? Engaging Teachers and Students in Dataset Specification for ML in Education](https://mlsanity.com/paper/2311.05792/) (2023)
- [Law and the Emerging Political Economy of Algorithmic Audits](https://mlsanity.com/paper/2406.11855/) (2024)
- [Stakeholder Participation for Responsible AI Development: Disconnects Between Guidance and Current Practice](https://mlsanity.com/paper/2506.09873/) (2025)
- [How Platform-User Power Relations Shape Algorithmic Accountability: A Case Study of Instant Loan Platforms and Financially Stressed Users in India](https://mlsanity.com/paper/2205.05661/) (2022)
- [Vibe Coding in Product Teams: Reconfiguring AI-Assisted Workflows, Prototyping, and Collaboration](https://mlsanity.com/paper/2509.10652/) (2025)
- [SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebooks](https://mlsanity.com/paper/2305.03039/) (2023)
- [FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines](https://mlsanity.com/paper/2307.15475/) (2023)
- [MLTEing Models: Negotiating, Evaluating, and Documenting Model and System Qualities](https://mlsanity.com/paper/2303.01998/) (2023)
- [Machine Learning practices and infrastructures](https://mlsanity.com/paper/2307.06518/) (2023)
- [Making Data Work Count](https://mlsanity.com/paper/2311.18046/) (2023)
- [Talking About the Assumption in the Room](https://mlsanity.com/paper/2502.13268/) (2025)
- [The Hall of AI Fears and Hopes: Comparing the Views of AI Influencers and those of Members of the U.S. Public Through an Interactive Platform](https://mlsanity.com/paper/2504.06016/) (2025)

## Abstract (arXiv)

Large and ever-evolving technology companies continue to invest more time and resources to incorporate responsible Artificial Intelligence (AI) into production-ready systems to increase algorithmic accountability. This paper examines and seeks to offer a framework for analyzing how organizational culture and structure impact the effectiveness of responsible AI initiatives in practice. We present the results of semi-structured qualitative interviews with practitioners working in industry, investigating common challenges, ethical tensions, and effective enablers for responsible AI initiatives. Focusing on major companies developing or utilizing AI, we have mapped what organizational structures currently support or hinder responsible AI initiatives, what aspirational future processes and structures would best enable effective initiatives, and what key elements comprise the transition from current work practices to the aspirational future.